Senior Data Engineer – AI, ETL, Dashboards (Remote)

Spectraforce Technologies, Inc.; Los Angeles (CA); Remote; USD 120,000 - 170,000; Full time

Job summary

Spectraforce Technologies, Inc. is seeking a Data Engineer for Analytics & AI to join as a founding data engineering partner. You will define analytical product features, write optimized SQL, and build end-to-end ETL pipelines on cloud platforms using Python.

You will develop dashboards in Tableau, perform ad-hoc analyses, and train ML models for clustering and prediction. The role involves AI agent support, RAG pipelines, and integrating MCPs or APIs for AI-driven insights.

Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
• 5+ years Python programming for scalable data pipelines and ETL on cloud infrastructure.
• 5+ years SQL querying and performance tuning on large datasets with cloud data warehouses (Snowflake/BigQuery/Redshift).
• 5+ years building and visualizing dashboards (Tableau/Power BI or similar).
• 4+ years building and evaluating ML models with PyTorch, TensorFlow or scikit-learn.

Responsibilities

• Partner with cross-functional teams to define analytical product features and requirements.
• Write and optimize SQL queries for large-scale data analysis.
• Design and build end-to-end ETL pipelines on cloud infrastructure using Python.
• Develop interactive dashboards in Tableau to visualize key metrics.

Skills

Python

SQL

ETL pipelines

Tableau/BI

Machine learning

Cloud data warehouses

Education

Bachelor's in CS/AI

Master's in CS/AI (preferred)

Tools

Snowflake

BigQuery

Redshift

Tableau

Job description

Spectraforce Technologies, Inc. is seeking a Data Engineer for Analytics & AI to join as a founding data engineering partner. You will define analytical product features, write optimized SQL, and build end-to-end ETL pipelines on cloud platforms using Python.

You will develop dashboards in Tableau, perform ad-hoc analyses, and train ML models for clustering and prediction. The role involves AI agent support, RAG pipelines, and integrating MCPs or APIs for AI-driven insights.